# Fine-tuning LLMs to 1.58bit: extreme quantization made easy

DevFeed: [Fine-tuning LLMs to 1.58bit: extreme quantization made easy](<https://devfeed.tech/articles/fine-tuning-llms-to-1-58bit-extreme-quantization-made-easy-6973.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/1_58_llm_extreme_quantization>)

Author: Mohamed Mekkouri; Marc Sun; Leandro von Werra; Pedro Cuenca; Omar Sanseviero; Thomas Wolf

Published: 2024-09-18T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [community](<https://devfeed.tech/tags/community.md>), [energy](<https://devfeed.tech/tags/energy.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [performance](<https://devfeed.tech/tags/performance.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [research](<https://devfeed.tech/tags/research.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

## AI overview

The article describes fine-tuning Llama models with the BitNet architecture at 1.58-bit ternary precision. It covers the resulting efficiency claims, benchmark comparison, and Transformers integration through BitLinear layers and a new "bitnet" quantization method.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.